Mac mini M4 64GBGünstige Wahl
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.7.3 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $1,099 MSRP
Can Qwen3.5 35B A3B run on MacBook Pro M3 Max 48GB?
YES — Tight Fit
C47Usable
Estimated from fit model
Qwen3.5 35B A3B needs ~31.5 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~11 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 31.5 GB, 11.2 tok/s, Tight fit
31.5 GB required34.6 GB available
91% VRAM used
Fit status
Tight fit
Decode
11.2 tok/s
TTFT
17222 ms
Safe context
28K
Memory
31.5 GB / 34.6 GB
Memory breakdown
Weights21.3 GB
KV Cache4.1 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsQwen3.5 35B A3B on MacBook Pro M3 Max 48GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 11.2 tok/s decode · 17.2s TTFT (warm) · 28 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 11.2 tok/s | 9394 ms | 28K |
| Coding | C | Tight fit | 11.2 tok/s | 17222 ms | 28K |
| Agentic Coding | C | Runs with offload (needs ~0.6 GB host RAM) | 10.6 tok/s | 26674 ms | 28K |
| Reasoning | C | Tight fit | 11.2 tok/s | 20354 ms | 28K |
| RAG | C | Runs with offload (needs ~0.6 GB host RAM) | 10.6 tok/s | 33342 ms | 28K |
Inference speed
Qwen3.5 35B A3B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Qwen3.5 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~56 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 56.2 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 26.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 21.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 20.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 17.7 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 16.6 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 11.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 10.6 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.3 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 9.7 | Heavy offload |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 6.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.7 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Qwen3.5 35B A3B (35B params) fits at each quantization level on MacBook Pro M3 Max 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C48 |
Q3_K_S | 3 | 17.2 GB | Low | C49 |
NVFP4 | 4 | 19.6 GB | Medium | C49 |
Q4_K_M | 4 | 21.3 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 25.2 GB | High | C49 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen3.5 35B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "lmstudio-community/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
Hardware, die Qwen3.5 35B A3B gut ausführt
MacBook Pro M4 Pro 64GBBestes Preis-Leistungs-Verhältnis
64 GB Unified (+16)
C
Raises estimated decode speed by about 58%.17.7 tok/s Dekodierung
Raises estimated decode speed by about 58%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,599 MSRP
MacBook Pro M3 Max 64GBApple-Upgrade
64 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.11.2 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Frequently asked questions
Can MacBook Pro M3 Max 48GB run Qwen3.5 35B A3B?
Yes, MacBook Pro M3 Max 48GB can run Qwen3.5 35B A3B with a C grade (Tight fit). Expected decode speed: 11.2 tok/s.
How much VRAM does Qwen3.5 35B A3B need?
Qwen3.5 35B A3B (35B parameters) requires approximately 31.5 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3.5 35B A3B?
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 35B A3B run at on MacBook Pro M3 Max 48GB?
On MacBook Pro M3 Max 48GB, Qwen3.5 35B A3B achieves approximately 11.2 tokens per second decode speed with a time-to-first-token of 17222ms using Q4_K_M quantization.
Can MacBook Pro M3 Max 48GB run Qwen3.5 35B A3B for coding?
For coding workloads, Qwen3.5 35B A3B on MacBook Pro M3 Max 48GB receives a C grade with 11.2 tok/s and 28K context.
What context window can Qwen3.5 35B A3B use on MacBook Pro M3 Max 48GB?
On MacBook Pro M3 Max 48GB, Qwen3.5 35B A3B can safely use up to 28K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for Qwen3.5 35B A3B?
Not always. MacBook Pro M3 Max 48GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-35b-a3b-gguf-on-m3-max-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: